Department of Wildlife Fish and Conservation Biology, University of California, Davis, United States of America; Department of Entomology, Cornell University, United States of America.
Department of Entomology and Nematology, University of California, Davis, United States of America.
Sci Total Environ. 2024 May 10;924:171591. doi: 10.1016/j.scitotenv.2024.171591. Epub 2024 Mar 12.
Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variation in pest ecology, but also from the widespread use of categorical land-use maps that do not adequately characterize habitat-availability for pests. We used 1163 field-year observations of Lygus hesperus (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely-sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because L. hesperus often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that L. hesperus densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.
景观生态学家长期以来一直认为，在简化的单一栽培景观中，害虫的数量会增加。然而，对这一理论的测试往往无法预测现实农业领域中的害虫种群数量。这些失败不仅可能源于害虫生态学的变化，还可能源于广泛使用的分类土地利用图，这些图不能充分描述害虫的栖息地可利用性。我们使用了加利福尼亚州棉花地中 1163 个实地观测年份的西方棉叶蝉（Lygus hesperus）密度数据，以确定将植被生产力和物候的遥感指标纳入害虫模型是否可以改进害虫丰度分析和预测。由于 L. hesperus 通常在非作物植被中越冬，我们预测，当农场周围的非作物植被较多时，害虫的数量会达到峰值，尤其是当非作物植被最初生产力较高，但在年初迅速枯竭，导致害虫扩散到棉花地中时。我们发现，非作物生境对害虫密度的影响因纬度而异，在北部呈正相关，在南部呈负相关。与我们的假设一致，模型预测，在高 versus 低生产力非作物植被（EVI 面积 350 vs. 50）环绕的农场中，L. hesperus 的密度要高出 35 倍，而在一年中休眠较早（5 月 15 日）时要高出 2.8 倍。尽管这些影响是强烈而显著的，但我们发现，与仅使用分类土地覆盖指标的模型相比，将这些遥感变量纳入土地利用模型仅略微提高了棉花中的害虫密度预测。总的来说，我们的工作表明，这里分析的遥感变量可以促进我们对害虫生态学的理解，但还不能实质性地提高害虫丰度预测的准确性。
